Atrial fibrillation (AF) is a condition during which there is disorganized electrical conduction in the atria, resulting in ineffective pumping of blood into the ventricle. If AF is detected, pharmacological cardioversion medication and electrical cardioversion are often effective at restoring and maintaining sinus rhythm. However, half of the recurrent episodes of AF are asymptomatic or “silent” and consequently can go undetected by a patient or physician. It is important to detect even asymptomatic AF because asymptomatic periods contribute to continued stroke risk and atrial remodeling.
Until recently, real time analysis of an intra-cardiac electrogram (EGM) has been based on coupling cardiac event detection with at least one algorithm that makes determinations based on sensed intervals. Generally, sensing cardiac events includes the reduction of information contained in the EGM to a binary event that signals the occurrence of atrial or ventricular activation. A sequence of such binary events is subsequently used to detect an abnormality, such as AF, by applying a process that analyzes a time relationship between the sensed binary events and triggers prescribed therapies based on the time relationship.
The introduction of real-time digital signal processing technology in implantable devices has added new dimensions to cardiac signal analysis. In addition to event based processing, digital signal processing technology enables morphological analysis of an EGM. There is great potential for the application of digital signal processing technology to combine algorithms based on morphological analysis with established event based algorithms to analyze cardiac signals and provide an early determination that asymptomatic AF is occurring with a patient.
Accordingly, it is desirable to provide a method and apparatus that enable detection of AF in patients, and particularly in patients that in which AF may be recurring. In addition, it is desirable to provide a method and apparatus that combines morphological analysis algorithms with event based analysis algorithms to enable detection of AF in patients. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.